700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ ReadersThis Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
Research Article Open Access
The growth of the Internet has made it much more difficult to effectively extract useful information from all the available online information. The overwhelming amount of data necessitates mechanisms for efficient information filtering. Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this paper we will look at three different recommender system approaches namely Collaborative filtering (CF), Content-based filtering, Hybrid recommender systems that can be used on different e-commerce websites. We briefly describe each type with pros and cons and will present some of the applications of Recommender Systems (RSs) in different domains.